{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 滤波代码\n",
    "import pywt\n",
    "from scipy import signal\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "def Wavelet_transform(ECG_Q):\n",
    "    w = pywt.Wavelet('db8') # 选用Daubechies8小波\n",
    "    maxlev = pywt.dwt_max_level(len(ECG_Q), w.dec_len)\n",
    "    threshold = 0.1 # Threshold for filtering\n",
    "    coeffs = pywt.wavedec(ECG_Q,'db8', level=maxlev) # 将信号进行小波分解\n",
    "    #这里就是对每一层的coffe进行更改\n",
    "    for i in range(1, len(coeffs)):\n",
    "        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i])) \n",
    "    datarec = pywt.waverec(coeffs, 'db8') # 将信号进行小波重构\n",
    "    return datarec\n",
    "\n",
    "def butter_fliter(data, frequency, highpass, lowpass):\n",
    "    [b, a] = signal.butter(3, [lowpass / frequency * 2, highpass / frequency * 2], 'bandpass')\n",
    "    Signal_pro = signal.filtfilt(b, a, data)\n",
    "    return Signal_pro\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(102496,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.io as scio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "ECG_01 = pd.read_csv(\"../Datasets/training_data.csv\")[\"x\"].to_numpy() # 训练集数据\n",
    "ECG_01.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 20:04:47,239 - root - INFO - --------------------------------------------------\n",
      "2023-05-04 20:04:47,239 - root - INFO - 载入ECG的信号, 长度 = 102496 \n",
      "2023-05-04 20:04:47,241 - root - INFO - 调用 christov_segmenter 进行R波检测 ...\n",
      "2023-05-04 20:04:48,741 - root - INFO - 完成 christov_segmenter 用时: 1.498485 秒. \n",
      "2023-05-04 20:04:48,741 - root - INFO - 调用 hamilton_segmenter 进行R波检测 ...\n",
      "2023-05-04 20:04:48,834 - root - INFO - 完成 hamilton_segmenter 用时: 0.091897 秒. \n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import logging\n",
    "import numpy as np\n",
    "from biosppy.signals import ecg\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 日志打印格式设定\n",
    "logging.basicConfig(level = logging.DEBUG,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s',force=True)\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "# record = 'ECG_Raw_0401'\n",
    "\n",
    "logging.info(\"--------------------------------------------------\")\n",
    "logging.info(\"载入ECG的信号, 长度 = %d \" % len(ECG_01))\n",
    "fs = 512  # 采样率为512 Hz\n",
    "logging.info(\"调用 christov_segmenter 进行R波检测 ...\")\n",
    "tic = time.time()\n",
    "rpeaks = ecg.christov_segmenter(ECG_01, sampling_rate=fs) # 调用christov_segmenter\n",
    "toc = time.time()\n",
    "logging.info(\"完成 christov_segmenter 用时: %f 秒. \" % (toc - tic))\n",
    "rpeaks_indices_1 = rpeaks[0]\n",
    "logging.info(\"调用 hamilton_segmenter 进行R波检测 ...\")\n",
    "tic = time.time()\n",
    "rpeaks = ecg.hamilton_segmenter(ECG_01, sampling_rate=fs) # 调用christov_segmenter\n",
    "toc = time.time()\n",
    "logging.info(\"完成 hamilton_segmenter 用时: %f 秒. \" % (toc - tic))\n",
    "rpeaks_indices_2 = rpeaks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(228,)\n",
      "(234,)\n"
     ]
    }
   ],
   "source": [
    "print(rpeaks_indices_1.shape)\n",
    "# print(rpeaks_indices_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 20:07:27,439 - root - INFO - 绘制波形图和检测的R波位置 ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 20:07:27,577 - root - INFO - 完成.\n"
     ]
    }
   ],
   "source": [
    "# 绘波形图和R波位置\n",
    "num_plot_samples = 1024*8\n",
    "logging.info(\"绘制波形图和检测的R波位置 ...\")\n",
    "sig_plot = ECG_01[:num_plot_samples]\n",
    "rpeaks_plot_1 = rpeaks_indices_1[rpeaks_indices_1 <= num_plot_samples]\n",
    "plt.figure()\n",
    "plt.plot(sig_plot, \"g\", label=\"ECG\")\n",
    "plt.grid(True)\n",
    "plt.plot(rpeaks_plot_1, sig_plot[rpeaks_plot_1], \"ro\", label=\"christov_segmenter\")\n",
    "rpeaks_plot_3 = rpeaks_indices_2[rpeaks_indices_2 <= num_plot_samples]\n",
    "plt.plot(rpeaks_plot_3, sig_plot[rpeaks_plot_3], \"b^\", label=\"hamilton_segmenter\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "logging.info(\"完成.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = len(ECG_01)\n",
    "ECG_Signal = np.empty(shape=[0,370])\n",
    "for site in rpeaks_indices_2:\n",
    "    if 185< site < length-185:\n",
    "        ECG_signal = ECG_01.flatten().tolist()[site-185:site+185]\n",
    "        ECG_Signal = np.row_stack((ECG_Signal,ECG_signal))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 前面已经处理成我们想要的ndarray的形状了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as scio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 标准化\n",
    "def standscaler_demo(date):\n",
    "    transfer = StandardScaler()\n",
    "    date1 = transfer.fit_transform(date)\n",
    "    return date1\n",
    "\n",
    "# 数据读进来处理的封装函数，只需要进行滤波操作和标准化操作\n",
    "def ECG_process(ECG):\n",
    "    ECG_filtered = np.zeros(ECG.shape)\n",
    "    for line in range(0,ECG.shape[0]):\n",
    "        ECG_filtered[line] = Wavelet_transform(ECG[line])\n",
    "    ECG_standed = standscaler_demo(ECG_filtered)\n",
    "    return ECG_filtered,ECG_standed   # 返回值为两个数组\n",
    "\n",
    "ECG_filtered,ECG_standed = ECG_process(ECG_Signal) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"ECG_filtered\",color=\"g\")\n",
    "plt.plot(ECG_filtered[0], color='g') \n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"ECG_Standed\",color=\"r\")\n",
    "plt.plot(ECG_standed[0], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 得到结论：ECG_filtered被用来训练效果应该是最好的，而不是经过标准化的ECG_standed\n",
    "#### 另外,我们也对数据进行了PCA降维处理,归一化等处理,结果都是不如数据直接经过滤波操作的效果好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(233, 370)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ECG_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "该图绘制的是ECG_filtered的第191条数据\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "# 模拟非正常心电\n",
    "ECG_stimulate = np.random.randint(0,1000,ECG_filtered.shape)\n",
    "ECG_stimulate =  ECG_stimulate + ECG_filtered # 之前是一团,现在是一个心电的形状\n",
    "# eg.\n",
    "random_num = random.randint(0,ECG_filtered.shape[0]) # 随机生成一个数、\n",
    "print('该图绘制的是ECG_filtered的第{}条数据'.format(random_num))\n",
    "plt.figure(figsize = (10, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"ECG_Right\",color=\"g\")\n",
    "plt.plot(ECG_filtered[random_num], color='g') \n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"ECG_Stimulate\",color=\"r\")\n",
    "plt.plot(ECG_stimulate[random_num], color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正确标签值列表 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "错误标签值列表 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "目标值向量是：\n",
      " [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "\n",
      "训练集ndarry格式的数据为:\n",
      " [[1664.32023172 1665.72145867 1668.18744727 ... 1737.59866462\n",
      "  1733.90877601 1736.24527928]\n",
      " [1800.68459797 1783.81825371 1793.91620841 ... 1627.01938438\n",
      "  1626.58241153 1626.24782694]\n",
      " [1710.75669956 1709.58218706 1707.04117728 ... 1657.72844302\n",
      "  1656.12407485 1659.53695852]\n",
      " ...\n",
      " [2642.73428279 2080.40257119 1752.66921104 ... 1809.38698721\n",
      "  2373.64854288 2511.02691588]\n",
      " [1843.72229189 1961.37885366 1957.54117881 ... 2114.58173509\n",
      "  2429.28313493 2497.13712435]\n",
      " [2134.08256886 2026.13320962 2287.14783427 ... 1900.7842658\n",
      "  2362.93145318 2454.93598256]]\n"
     ]
    }
   ],
   "source": [
    "#  构建数据集\n",
    "true_list = [1] * ECG_filtered.shape[0]\n",
    "print(\"正确标签值列表\", true_list)\n",
    "error_list = [0] * ECG_stimulate.shape[0]\n",
    "print(\"错误标签值列表\", error_list )\n",
    "for i in error_list:\n",
    "    true_list.append(i)\n",
    "target_list = true_list\n",
    "print(\"目标值向量是：\\n\",target_list)\n",
    "\n",
    "#  合并两个训练集为ndarray类型的数据\n",
    "np_train = np.vstack((ECG_filtered,ECG_stimulate))\n",
    "print(\"\\n训练集ndarry格式的数据为:\\n\", np_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分割后用来训练的特征值是:\n",
      " [[1676.66177205 1697.4510121  1702.72423082 ... 1638.27967836\n",
      "  1646.14279805 1650.3177567 ]\n",
      " [1788.18509142 1785.64359583 1789.64216934 ... 1718.59218309\n",
      "  1712.4762109  1717.36397725]\n",
      " [2678.07198812 2226.97993244 1773.53464194 ... 2619.30071368\n",
      "  1807.28119221 2172.89025819]\n",
      " ...\n",
      " [1828.44173385 2448.3434456  1875.8584665  ... 2149.28443259\n",
      "  2335.65367589 2075.16415568]\n",
      " [2307.59962846 1861.97189658 1913.30862825 ... 1771.94833298\n",
      "  2337.98204819 2499.523167  ]\n",
      " [1784.47082947 1784.54592614 1784.77334779 ... 1750.88253753\n",
      "  1751.44427252 1751.72712645]]\n",
      "\n",
      "分割后用来测试的特征值是:\n",
      " [[1750.36959144 1750.43461698 1750.5915741  ... 1759.9124595\n",
      "  1760.18746752 1760.1944602 ]\n",
      " [2395.86668316 1774.92056324 2478.76946501 ... 2485.35896439\n",
      "  2317.87711047 1889.02618398]\n",
      " [2367.02523332 2647.69135984 2426.38405404 ... 2175.18827625\n",
      "  2142.17099002 1717.65660002]\n",
      " ...\n",
      " [2640.12143221 2701.27218888 2313.28178225 ... 2460.28284656\n",
      "  2648.48269904 2513.58297615]\n",
      " [2140.39863006 2367.93246241 1709.98238757 ... 1886.34202501\n",
      "  1726.06785142 2027.27539402]\n",
      " [2427.14914085 2508.91065625 2707.42480614 ... 2108.88896942\n",
      "  1917.13128007 2102.13341217]]\n",
      "\n",
      "分割后用来训练的目标值是:\n",
      " [1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1]\n",
      "\n",
      "分割后用来测试的目标值是:\n",
      " [1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "#  进行数据分割\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(np_train, target_list, test_size=0.2)\n",
    "\n",
    "print(\"\\n分割后用来训练的特征值是:\\n\", x_train)\n",
    "print(\"\\n分割后用来测试的特征值是:\\n\", x_test)\n",
    "print(\"\\n分割后用来训练的目标值是:\\n\", y_train)\n",
    "print(\"\\n分割后用来测试的目标值是:\\n\", y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择机器学习中的SVC\n",
    "原因:\n",
    "1.选择机器学习的原因\n",
    "    (1)训练集的数据量不大,相较于深度学习,这个二分类模型就是一个简单的弱学习器,总之,可以用深度学习提取特征并进行训练,但是是没有必要的\n",
    "    (2)使用机器学习能够使代码的健壮性更强,虽然这个二分类模型是一个弱学习器,仅可以区分正常心电和加入了高斯噪声的心电,但是完全可以加上很多个弱学习器使整个大模型变成一个强学习器,可以做一个很多弱学习器融合的集成学习,使得模型更加健壮\n",
    "2.选择SVC的原因:\n",
    "    在使用这个模型之前(two_classification_v1),我们小组也尝试过有监督学习分类算法的其他常规模型:KNN,逻辑回归,决策树,随机森林,但是模型最终的准确率都不如向量机,虽然我们使用的sklearn库将算法封装在里面的,相当于一个黑盒子,但是我们仍需对常见的机器学习算法做一定了解,经过一番思考,我们小组做出如下推测:和KNN,逻辑回归这几类算法将预测值用一个平面或者曲面不同,向量机算法是通过建立一个超平面将目标值所分开,心电数据作为一种时间序列,在我们没有经过波形提取之前,它的峰值出现在哪一个维度本身就是不确定的,就导致了最后所截取的心拍完全交错开来了,所以想要将不同目标的心电用一个平面或者一个曲面分割开来,这几乎是不可能的,所以如果使用这几类模型,评估器只能乱猜了,导致最后的准确路在百分之五十上下波动\n",
    "    但是在使用这个模型之后(two_classification_v2),我认为完全可以使用KNN,logistics regression,因为这一次我们是将心拍最中间的值提取为R峰峰值,这样用平面或者曲面分开是有可能的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "向量机的预测值是：\n",
      " [1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 0 0 0 1\n",
      " 1 1 1 1 1 0 0 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0 1 0 1 0 0 1 1 0 1 1 0 0 1\n",
      " 0 0 1 1 1 0 1 1 1 0 1 0 1 1 1 0 1 0 0 0]\n",
      "实际值是:\n",
      " [1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0]\n",
      "准确率是：\n",
      " 1.0\n",
      "auc指标是:\n",
      " 1.0\n"
     ]
    }
   ],
   "source": [
    "# 向量机\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import pickle\n",
    "\n",
    "estimator = SVC(random_state=6)\n",
    "Svc_train = estimator.fit(x_train, y_train)\n",
    "svc_tra = estimator.score(x_test, y_test)\n",
    "\n",
    "# 模型保存\n",
    "with open (\"II. Classification(SVC)2.pickle\",\"wb\") as f:\n",
    "    pickle.dump(Svc_train,f) #将训练好的模型svc_tra存储在变量f中，且保存到本地\n",
    "\n",
    "# 模型的加载和使用\n",
    "with open(\"II. Classification(SVC)2.pickle\",\"rb\") as f:\n",
    "    SVM_load = pickle.load(f)\n",
    "\n",
    "y_pre_svc = estimator.predict(x_test)\n",
    "roc_svc_score = roc_auc_score(y_test, y_pre_svc)\n",
    "svc_tre = estimator.score(x_test, y_test)\n",
    "print(\"向量机的预测值是：\\n\", y_pre_svc)\n",
    "print(\"实际值是:\\n\", y_test)\n",
    "print(\"准确率是：\\n\", svc_tre)\n",
    "print(\"auc指标是:\\n\",roc_svc_score) \n",
    "# II. Classification2"
   ]
  }
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